Wireline Logs and Core Data Integration in Los Molles Formation, Neuquen Basin, Argentina
Bibliographic record
Abstract
Abstract In Agua del Cajón Block (Neuquen Basin, Argentina), petrophysical data was treated according to deductive and inductive methods in order to get the most from both approaches. Deductive methods comprise those methodologies that seek to differentiate the data by the computation of a set of component proportions whose identification is linked with wireline log data by a set of response equations. The model is built considering the number of components and the number of variables (data curves). Normally, measures to detect mismatches and gross errors are included in the techniques, although mathematical consistency is not a guarantee of geological accuracy. This situation is well represented by standard log analysis. On the contrary, inductive methods establish their classes or transformations based on the data set and do not depend on any predetermined correlation among the components. These methods tend to isolate distinctive patterns and to derive classifications or new variables that can be interpreted with a physical meaning. Cluster analysis is one example of these types of methodologies. In this study, wireline logs (spontaneous potential, gamma ray, shallow-medium and deep resistivities, neutron, density, sonic, photoelectric factor, and microrresistivity images) were calibrated according to the lithological variations – facies- described in cores taken from the reservoirs developed within Los Molles Formation. These facies which comprised crudely bedded gravel with imbrication (Gm), medium to coarse, even pebbly sand with planar crossbeds (Sp), very fine to very coarse sand with horizontal lamination (Sh) and massive mud-silt (Fm), were linked with electrofacies using multivariate analysis, particularly cluster analysis. The first two facies are the actual reservoirs targets within the field. After the analysis of ten wells within the field, a more thorough understanding of the petrophysical properties and a deeper understanding of the dynamic responses of the reservoirs was achieved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".